Resin-modified glass ionomer cement tends to shrink due to polymerization of the resin component. Additionally, they are more prone to syneresis and imbibition during the setting process. This in vitro study evaluates the impact of chitosan, a biopolymer that is, both biomaterial and biocompatible, on the strength of dentin bonding and compares it with ACTIVA Bio-ACTIVE Restorative. The present study was aimed to assess the impact of including chitosan into Fuji II on the shear bond strength between. the restoration material and tooth dentin,

Objective. This study aimed to evaluate the orthodontic bond strength and enamel-preserving ability of a hydroxyapatite nanoparticles-containingself-etch system following exposure to various ageing methods. Materials and Methods. Hydroxyapatite nanoparticles (nHAp) were incorporated into an orthodontic self-etch primer (SEP, Transbond™ plus) in three different concentrations (5%, 7%, and 9% wt) and tested versus the plain SEP (control) for shear bond strength (SBS), adhesive remnant index (ARI) scores, and enamel damage in range-finding experiments using premolar teeth. The best-performing formulation was further exposed to the following four artificial ageing methods: initial debonding, 24 h water storage, one-month water stora

The research aims to show the relationship between artificial intelligence in accounting education and its role in achieving sustainable development goals in the Kingdom of Bahrain. The research dealt with the role of artificial intelligence applications in accounting education at the University of Applied Sciences as a model for Bahraini universities to achieve sustainable development goals. The application of artificial intelligence in accounting education achieves seven of the seventeen sustainable development goals. It also concludes that there is an artificial intelligence infrastructure in the Kingdom of Bahrain, as it occupies a leading regional position in digital transformation, as Bahrain ranks first in the Arab world i

This research aims to clarify the importance of an accounting information system that uses artificial intelligence to detect earnings manipulation. The research problem stems from the widespread manipulation of earning in economic entities, especially at the local level, exacerbated by the high financial and administrative corruption rates in Iraq due to fraudulent accounting practices. Since earning manipulation involves intentional fraudulent acts, it is necessary to implement preventive measures to detect and deter such practices. The main hypothesis of the research assumes that an accounting information system based on artificial intelligence cannot effectively detect the manipulation of profits in Iraqi economic entities. The researche

   In this research, dynamical study of an SIR epidemical model with nonlinear direct incidence rate (Beddington-De Angelis ) type, and regress of treatment investigated .An  analytical study  to the model shows that there are two equilibrium points appear, the discussed successfully with sufficient condition, the existence of local bifurcation and Hopf bifurcation was analyzed, finally numerical simulations are done to explain the analytic studies.

In this paper, numerical and experimental studies on the elastic behavior of glass fiber reinforced ‎polymer (GFRP) with stiffeners in the GFRP section's web (to prevent local buckling) are presented. ‎The GFRP profiles‎ were connected to the concrete deck slab by shear connectors. Two full-scale simply supported ‎composite beams (with and without stiffeners) were tested under impact load (three-point load) to ‎assess its structural response. The results ‎proved that the maximum impact force, maximum ‎deflection, damping time, and ‎damping ratio of the composite beam were affected by the GFRP ‎stiffeners‎. The experimental results indicated that the damping ratio and deflection were diminished compare

Software-defined networking (SDN) is an innovative network paradigm, offering substantial control of network operation through a network’s architecture. SDN is an ideal platform for implementing projects involving distributed applications, security solutions, and decentralized network administration in a multitenant data center environment due to its programmability. As its usage rapidly expands, network security threats are becoming more frequent, leading SDN security to be of significant concern. Machine-learning (ML) techniques for intrusion detection of DDoS attacks in SDN networks utilize standard datasets and fail to cover all classification aspects, resulting in under-coverage of attack diversity. This paper proposes a hybr